智能ai深度学习技术
As physicians, nurses, dentists, or any healthcare expert, we all have experienced the earshot of floating buzzwords about the themes of Artificial intelligence (AI), machine learning (ML), and deep learning (DL). But not all of us are mindful of their potential consequences. On the contrary, yet generally speaking, most people, particularly the millennials, seem to be sparkly optimistic about the role of Artificial intelligent technology as being collectively encouraging.
作为医生，护士，牙医或任何医疗保健专家，我们都曾听到过有关人工智能(AI)，机器学习(ML)和深度学习(DL)主题的流行词汇。 但是，并非所有人都意识到他们的潜在后果。 相反，从总体上讲，大多数人，特别是千禧一代，似乎对人工智能技术在集体鼓励中的作用一闪而过。
Deep learning is a component of a much more comprehensive group of technology termed machine learning. DL defines the spectrum of artificial neural networks amidst imitation learning. Accurately why deep learning is also referred to as deep structured learning or differential programming, which can adopt a form of supervised, semi-supervised, or unsupervised modalities. Deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been mainly applied to domains such as speech recognition, natural language processing, computer vision, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs. Each and every component of the DL techniques have produced results analogous to human expertise, and even better.
深度学习是更广泛的称为机器学习的技术组的组成部分。 DL定义了模仿学习中人工神经网络的范围。 准确地将深度学习为何也称为深度结构化学习或差异编程的原因，可以采用监督，半监督或无监督的形式。 深度神经网络，深度信念网络，递归神经网络和卷积神经网络已主要应用于语音识别，自然语言处理，计算机视觉，音频识别，社交网络过滤，机器翻译，生物信息学，药物设计，医学等领域图像分析，材料检查和棋盘游戏程序。 DL技术的每个组成部分都产生了类似于人类专业知识的结果，甚至更好。
In general, the concept of machine learning follows; that the contraption should be able to learn and adapt through experience and execute the tasks “smartly.”
通常，机器学习的概念如下： 该设备应该能够通过经验学习和适应并“聪明地”执行任务。
Artificial Intelligence implements whatever learned by way of machine learning, deep learning, and other systems to solve substantive predicaments. In the computer science realm, artificial intelligence (AI), also referred to as machine intelligence, is nothing but machines’ capacity to demonstrate, what is typical for natural intellect exhibited by humans and animals.
人工智能通过机器学习，深度学习和其他系统来实现所学到的一切，从而解决实质性难题。 在计算机科学领域，人工智能(AI)，也称为机器智能，无非是机器的展示能力，这是人类和动物表现出的自然智力的典型特征。
人工智能，机器学习的效用 (The Utility of Artificial Intelligence, Machine Learning)
With Artificial intelligence, today, one can perform an extraordinary spectrum of tasks. Using AI, one can ask questions by voice and get answers about a multitude of issues not stereotypically known to everyone. Or The computer can find data that could never come to a person’s mind. Artificial Intelligence, utilizing Deep Learning, will offer a narrative summary of someone’s data and suggest other ways to probe into collected information. Similarly, AI will furthermore distribute information narrated to earlier inquiries from others who asked the same questions. You’ll get the answers on a screen or directly through conversation.
如今，借助人工智能，人们可以执行各种任务。 使用AI，人们可以通过语音提出问题，并获得有关每个人都没有刻板印象的众多问题的答案。 或者计算机可以找到永远不会想到的数据。 人工智能将利用深度学习来提供某人数据的叙述性摘要，并提出探究收集到的信息的其他方法。 同样，人工智能将进一步分发讲述早期询问的信息，这些信息来自提出相同问题的其他人。 您将在屏幕上或直接通过对话获得答案。
The utility of artificial intelligence and Deep Neural Learning may seem potentially legit and promising, particularly concerning the extension of quality human life. Nonetheless, in realism, the messages portrayed are varied. Indeed, In health care, treatment efficacy can be determined instantly, whereas, in retail, inventories suggested quickly, or in finance, fraud prevented instead of just spotted. In each and every latter scenario, the computer efficiently recognizes what information is necessitated, looks at relationships between all the factors, forms an answer, and automatically communicates it to the users. It provides options for follow-up queries and even carries out additional pre-determined tasks with little human intervention yet even better.
人工智能和深度神经学习的效用似乎具有潜在的合法性和前途，特别是在延长人类优质生活方面。 但是，现实中所描绘的信息是多种多样的。 的确，在医疗保健中，可以立即确定治疗效果，而在零售中，可以Swift建议库存，或者在财务中，可以防止欺诈，而不仅仅是发现欺诈。 在每种情况下，计算机都可以有效地识别出需要哪些信息，查看所有因素之间的关系，形成答案并将其自动传达给用户。 它提供了后续查询的选项，甚至可以在几乎没有人为干预的情况下甚至可以更好地执行其他预定任务。
Every AD, ML, DL technology relies on a set of finite sequences of explicit, computer-implementable instruction or algorithms, which frequently not disclosed to the public. Consequent to everything mentioned, the notion of Artificial Intelligence utility is a bittersweet experience, as the risk versus benefit of the technology lies within its particular algorithm.
每种AD，ML，DL技术都依赖于一组明确的，计算机可实现的指令或算法的有限序列，而这些序列通常不会公开。 因此，由于提到了该技术的风险与利益在于其特定算法中，因此，人工智能效用的概念是苦乐参半的体验。
Artificial intelligence delivers the promise of genuine human-to-machine interaction. It literally magnifies human potential with cumulative precision. The intelligent machines, over time, utilizing various machine learning techniques, can understand requests irrespective of a good deed or evil feat.
人工智能带来了真正的人机交互的希望。 从字面上看，它以累积的精度放大了人类的潜力。 随着时间的流逝，智能机器利用各种机器学习技术，可以理解请求，而不管行为是好是坏。
Artificial Intelligence help connect data points and draw conclusions irrespective of moral consequence, while they can learn to reason, observe, and plan.
人工智能可以帮助连接数据点并得出结论，而不论其道德后果如何，而人工智能则可以学习推理，观察和计划。
All the advancements from Amazon Alexa to Apple Siri brought artificial intelligence closer to its original goal of creating intelligent machines, which we’re starting to see more and more in our everyday lives. From recommendations on our favorite retail sites to auto-generated photo tags on social media, many ordinary online amenities are powered by artificial intelligence. Further, we see thru advances in AI technologies, the more the privacy goes out the door, and the farther trivial turn out to be, our individual liberty.
从Amazon Alexa到Apple Siri的所有进步使人工智能更接近其最初的目标，即创建智能机器，这一点在我们的日常生活中越来越多地看到。 从我们最喜欢的零售网站上的推荐到社交媒体上的自动生成的照片标签，许多普通的在线便利设施都由人工智能提供支持。 此外，我们看到了AI技术的飞速发展，隐私权越多，个人自由就越是微不足道。
医疗保健中的人工智能 (Artificial Intelligence in Healthcare)
Artificial intelligence is becoming a transformational force in the healthcare arena, as expected to disrupt healthcare in many ways.
人工智能正在成为医疗保健领域的变革力量，正如人们期望的那样，它将以多种方式破坏医疗保健。
人工智能有望通过界面将人的思想与机器的思想统一起来。 (Artificial Intelligence is expected to unify the Human Mind with that of the Machine through an Interface.)
Establishing a direct connection between technology and the human brain without the use of keyboards, mouse, and monitors is a state-of-the-art research theme that has abundant applications towards patient care. It will, for example, take up some of the responsibilities for kinds of functions that could be potentially taken away by some Neurological diseases and trauma to the nervous system. Or AI will be able to speak for the patient when impaired otherwise move his arm if paralyzed.
在不使用键盘，鼠标和显示器的情况下，在技术与人脑之间建立直接连接是一项最新的研究主题，在患者护理方面具有广泛的应用。 例如，它将承担某些功能的某些责任，这些功能可能会被某些神经系统疾病和神经系统创伤所带走。 否则，AI可以在患者受损时代言，否则，瘫痪时请移动手臂。
下一代人工智能将进行放射学阅读。 (The Next Generation of Artificial Intelligence will perform Radiological Readings.)
Radiological images captured by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner workings of the human anatomy. Though several diagnostic processes still rely on direct tissue sampling or tissue biopsy to carry risks of infection and bleeding, AI will actually enable the next generation of radiology machines thorough enough to omit the need for diagnostic biopsy in selected instances.
MRI机器，CT扫描仪和X射线捕获的放射图像可提供对人体解剖结构内部工作的非侵入性可见性。 尽管一些诊断过程仍然依靠直接的组织采样或组织活检来承担感染和出血的风险，但AI实际上将使下一代放射学机器足够彻底，从而在某些情况下无需进行诊断活检。
Artificial intelligence is enabling “virtual biopsies” by advancing the innovative field of “radiomics.” The following science emphases on harnessing image-based algorithms to portray the phenotypes and genetic properties of tumors.
人工智能通过推动“放射学”的创新领域来实现“虚拟活检”。 以下科学强调利用基于图像的算法来描绘肿瘤的表型和遗传特性。
人工智能将最大程度地为服务欠佳和农村社区提供优质医疗服务。 (Artificial Intelligence will maximize Quality Medical Care to Underserved and Rural Communities.)
Shortages of qualified physicians, including radiology technicians and radiologists, can potentially curb admittance to life-saving care in developing communities around the globe.
合格的医师(包括放射技师和放射医师)的短缺可能会限制全球发展中社区对挽救生命的护理的接受。
Artificial intelligence could help alleviate the repercussions of a severe deficit of qualified clinical staff by taking over some of the responsibilities typically earmarked to humans.
人工智能可以通过接替通常指定给人类的职责来减轻合格临床人员严重短缺的影响。
使用适当的AI算法，电子病历(EHR)可以更有效。 (Electronic Health Records (EHR) can be more efficient using appropriate AI Algorithms.)
Electronic health records are playing a more active part progressively in the healthcare industry’s drive towards documentation and The Health Information Technology for Economic and Clinical Health (HITECH). However, the transition to the digitalization of health records has faced innumerable problems, from cognitive overload, continual documentation, to physician burnout.
电子病历在医疗保健行业向文档和“经济和临床健康卫生信息技术”(HITECH)迈进的过程中逐渐发挥着越来越积极的作用。 然而，从认知超负荷，持续记录到医生精疲力尽，向病历数字化的过渡面临无数问题。
HITECH industry is now using AI and deep learning to create more spontaneous interfaces by automating some of the formal rules that occupy most of the physician’s time. Most likely than not, machine learning and AI may further support preparing conventional requests from the inbox, like medication refills and results from notifications. It may additionally assist in prioritizing tasks that truly require the clinician’s awareness.
HITECH行业现在正在使用AI和深度学习通过使占用医生大部分时间的一些正式规则自动化来创建更多自发的界面。 很有可能，机器学习和AI可能会进一步支持准备来自收件箱的常规请求，例如药物补充和通知结果。 它还可以帮助确定真正需要临床医生意识到的任务的优先级。
人工智能将把医疗设备变成一个独立运行的机器人。 (Artificial Intelligence will turn a Medical Device into an Independently functioning Robot.)
Smart medical devices are filling up the user scene, allowing everything from real-time video from the inside of an intestine to sensing facial expression for early diagnosis of Autism.
智能医疗设备填补了用户的空白，允许从肠道内部的实时视频到感知面部表情等一切事物，以早期诊断自闭症。
In the medical setting, smart machines are decisive for monitoring patients across various spectrums of sceneries, from ICU to home care. Using AI physicians will benefit from enhanced ability to identify various pathologies deterioration, such as if sepsis is imminent, or detect the development of complications before it happens, hence significantly improving clinical outcomes and may reduce costs related to hospital-acquired condition forfeits.
在医疗环境中，智能机器对于监控从ICU到家庭护理的各种场景的患者至关重要。 使用AI医师将受益于增强的识别各种病理恶化的能力，例如败血症迫在眉睫，或者在并发症发生之前就检测出并发症的发展，从而显着改善临床结果，并可以减少因医院获得的病情而造成的费用。
人工智能可以帮助避免抗生素耐药性的风险。 (Artificial Intelligence can help avert Risks of Antibiotics-Resistance.)
Antibiotic resistance is a growing peril to populations around the ecosphere as the overuse of these essential medications fosters the evolution of certain strains of bacteria that fail to respond to future therapies.
由于过度使用这些基本药物会导致某些无法对未来疗法产生React的细菌进化，因此抗生素抗药性正在日益威胁着生态圈的人们。
精确分析病理图像 (Analyzing Pathologic images with Precision)
Today, pathological specimens provide over 70% of the sources of diagnostic data for physicians across the spectrum of care delivery. And almost all the extracted data is widely available within the electronic health record systems. So the more precise we become, and the sooner we get to the right diagnosis, the better we’re going to be, making digital pathology, data and the AI an invaluable opportunity to deliver better medical care.
如今，病理标本为整个医疗服务范围内的医生提供了超过70％的诊断数据来源。 几乎所有提取的数据都可以在电子健康记录系统中广泛使用。 因此，我们变得越精确，越早做出正确的诊断，我们就会越好，这将使数字病理学，数据和AI成为提供更好医疗服务的宝贵机会。
Deep learning algorithms and Artificial Intelligence analytics that can drill down to the minute precision on large digital images, thus allowing physicians to pinpoint subtleties that may skip the human eye. AI can further enhance productivity through the identification of features of concern in pathological preparations before human clinician studies the data.
深度学习算法和人工智能分析可以细化到大型数字图像的精确度，从而使医生能够找出可能会跳过人眼的细微之处。 AI可以通过在临床医生研究数据之前识别病理准备中的相关特征来进一步提高生产力。
深度学习和AI对于免疫疗法和基于基因组的癌症治疗具有无价的价值。 (Deep learning and AI are invaluable for Immunotherapy and Genomic based Cancer Treatment.)
Immunotherapy is one of the most astonishing achievements in cancer remedy. It teaches and uses the body’s own immune response to attack malignancies. Deep learning algorithms and their artificial intelligence upshots promote the synthesis of highly sophisticated datasets that formulates precise decisions for targeted therapies in the direction of individual cancer’s sole genetic structure.
免疫疗法是癌症治疗中最令人惊讶的成就之一。 它教导并利用人体自身的免疫React来攻击恶性肿瘤。 深度学习算法及其人工智能成果促进了高度复杂的数据集的合成，这些数据集针对个体癌症唯一的遗传结构，为靶向疗法制定了精确的决策。
人工智能可以通过改变电子健康记录来增强患者风险分层。 (Artificial Intelligence can potentiate Patient Risk stratification by Transforming the Electronic Health Record.)
Patient’s medical records are a goldmine of personal data, however extracting and analyzing such a wealth of information in a precise, timely, and consistent manner has been a continual challenge for physicians and data analysts.
患者的病历是个人数据的金矿，但是以精确，及时和一致的方式提取和分析如此大量的信息一直是医师和数据分析人员面临的持续挑战。
Data quality and probity problems, as well as a mixture of data setups, makes the task complicated. Moreover, whether inputs are structured or not, along with incompleteness records, make understanding of exactly how to engage in meaningful risk stratification, predict analytics, and support clinical decision making extremely difficult.
数据质量和概率问题以及数据设置的混合使任务变得复杂。 此外，无论输入是否经过结构化，再加上不完整的记录，都将使您确切地了解如何进行有意义的风险分层，预测分析并支持极其困难的临床决策。
EHR analytics have produced many thriving risk scoring and stratification tools. Yet, Amidst all, researchers apply DL methods to classify unique associations between seemingly irrelevant datasets.
EHR分析产生了许多繁荣的风险评分和分层工具。 然而，最重要的是，研究人员应用DL方法对看似无关的数据集之间的唯一关联进行分类。
使用智能机器可进一步增强通过可穿戴设备监控健康状况的功能。 (Monitoring Health status through Wearable devices is farther enhanced using Smart Machines.)
With the increasing accessibility to wearable devices that use sensors to collect valuable consumer health data and transmit over smartphones, their utility is becoming more than ever inevitable. For instance, with step trackers, one can continuously track a heart pulse. In short, By implementing such technology, a growing portion of health-related data is generated on the go.
随着使用传感器收集有价值的消费者健康数据并通过智能手机传输的可穿戴设备的可访问性越来越高，其实用性正变得越来越不可避免。 例如，使用步进跟踪器，可以连续跟踪心脏脉冲。 简而言之，通过实施这种技术，在旅途中会生成越来越多的健康相关数据。
Collecting and analyzing medical information and supplementing it with data obtained from patients through apps and other home monitoring devices can contribute a matchless viewpoint into individual and population well-being. Therefore, Artificial intelligence can play a significant part in extracting actionable insights from this massive and endless treasure of data.
收集和分析医疗信息，并补充通过应用程序和其他家庭监控设备从患者那里获得的数据，可以为个人和人群带来无与伦比的观点。 因此，人工智能可以在从海量无休止的数据宝藏中提取可行的见解中发挥重要作用。
智能手机自拍是临床考试的未来工具 (Smartphone Selfies are the future Tool for the Clinical Exam)
Harnessing the potential of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an essential supplement to clinical quality imaging, particularly in underserved populations or developing nations.
专家们认为，利用便携式设备的潜力，从智能手机和其他消费级来源获取的图像将是临床质量成像的重要补充，尤其是在服务不足的人群或发展中国家。
The quality of cell phone cameras is growing by the year, as they can yield images that are viable for analysis utilizing artificial intelligence algorithms. Such technologies are very well known to modern Dermatology and ophthalmology.
手机摄像头的质量正在逐年提高，因为它们可以生成可用于利用人工智能算法进行分析的图像。 这样的技术在现代皮肤病学和眼科学中是众所周知的。
British Researchers have even developed a means that identifies developmental anomalies by analyzing images of a child’s face in the womb.
英国研究人员甚至已经开发出一种方法，可以通过分析子宫中孩子的脸部图像来识别发育异常。
人工智能正在彻底改变医师进行诊断检查的方式。 (Artificial Intelligence is Revolutionizing the way Physicians will do their Diagnostic Workup.)
As the healthcare industry is drifting away from fee-for-service reimbursement system, towards a merit-based compensation model, so, is it moving further and further from “reactive care” to treating the already manifesting disease to addressing the problem before symptoms appear, hence “proactive care.” Artificial intelligence will lay the grounds for that diagnostic revolution by powering predictive analytics and clinical judgment guide instruments that will alert physicians with obstacles long before they might otherwise recognize the need to tackle.
随着医疗保健行业逐渐从按服务付费的报销系统过渡到基于绩效的报酬模型，它是否正在从“React式护理”转向治疗已经表现出的疾病，再到症状出现之前解决问题，因此称为“主动护理”。 人工智能将为预测性分析和临床判断指导工具提供动力，从而为这一诊断革命奠定基础，这些预警工具将在医生意识到可能需要解决的很长时间之前向他们发出警告。
深度学习有什么帮助 (What is helpful about Deep learning)
Deep learning carries valuable potential for real-world applications. Traditionally, machine learning described the training methods by which pictures used to train the program were tagged with the name of the thing in the picture.
深度学习为现实应用带来了宝贵的潜力。 传统上，机器学习描述了一种训练方法，通过该方法将用于训练程序的图片标记为图片中事物的名称。
The traditional machine learning scheme typically uses the photo and matches it with the “Tag” included within the image. The latter ML technique is referred to as “supervised learning.”
传统的机器学习方案通常使用照片，并将其与图像中包含的“标签”进行匹配。 后者的ML技术称为“监督学习”。
Do you recall tagging your photo or your friend’s photo with his or her name on Facebook posts? — That is how the ML would be able to learn a person’s face, identify it amid all others, and match it with other identifying factors on the internet for future authentication and identification.
您还记得在Facebook帖子上用您的名字或您的朋友的名字标记您的照片吗？ —这就是ML能够学习一个人的面Kong，在所有其他面Kong中进行识别并将其与Internet上的其他识别因素进行匹配的方式，以便将来进行身份验证和识别。
Supervised learning is fast and demands comparatively less computational power than some other training techniques used in machine learning. However, It has a significant drawback for real-world applications. Every day, an immense amount of information about people is gathered from social media, hardware, and software service contracts, app authorizations, and website cookies.
与机器学习中使用的其他一些训练技术相比，监督学习是快速的并且需要相对较少的计算能力。 但是，对于实际应用而言，它具有明显的缺点。 每天，都会从社交媒体，硬件和软件服务合同，应用程序授权以及网站cookie中收集大量有关人员的信息。
The ML aided data mining or personal information collection is precious to businesses at all levels with a variety of agendas. The problem is that all of the said data is untagged and can’t be used to teach machine learning programs that depend on supervised learning. Because it still requires a person’s help to label or tag the data, which is not only time-consuming but also a costly process.
机器学习(ML)辅助数据挖掘或个人信息收集对于具有各种议程的各个级别的企业而言都是宝贵的。 问题在于，所有上述数据都是未加标签的，并且不能用于教授依赖监督学习的机器学习程序。 因为仍然需要人的帮助来标记或标记数据，所以这不仅耗时，而且成本很高。
Deep learning networks can bypass traditional ML shortcomings because they utilize the so-called “unsupervised learning.” DL, do not require data labeling or tagging. Even though the pictures don’t come with the name “Tag,” Instead, Deep neural networks will still learn to identify the person.
深度学习网络可以利用传统的ML缺点，因为它们利用了所谓的“无监督学习”。 DL，不需要数据标签或标签。 即使图片中没有名称“ Tag”，但深度神经网络仍将学会识别人。
The ability to learn from un-tagged or unorganized data is a tremendous advantage for those interested in real-world applications. Deep learning unlocks the treasure trove of big unstructured data for those with the imagination to use it.
对于那些对实际应用程序感兴趣的人，从未标记或未组织的数据中学习的能力是一个巨大的优势。 深度学习为那些有想象力的人打开了大型非结构化数据的宝库。
人工智能和深度学习(如果正确使用)可以补充个性化医疗服务 (Artificial intelligence and deep learning, if used right can complement personalized medical care)
21st-century’s millennial vision of physicians and healthcare is still about maintaining Hippocratical personalized medicine while sustaining the quality medical care using state of the art technology. Concomitantly the medical community is losing sanity by rapid putsch of sacred clinical judgment to a protocol based unyielding algorithmic patient care. The old fashioned population health model is one of the reasons to blame for such a course. But Artificial intelligence, if obtained through transparent and accountable methods, can lay the foundation for the personalized healthcare system. Deep learning technology can learn everything about a patient from birth onward and preserve in a decentralized fashion (Using Blockchain technology) without exposing personal information to alternate use. The data collected and held by the individual patient, physician, or any other user as the sole owner of their data will be able to take advantage of the unsupervised DL technology to help them take advantage of the kind of personalized care they want and need. Centralized big data processing will only benefit other industries and further contaminate the already flawed population health model.
21世纪医师和医疗保健的千禧年愿景仍然是保持希波克拉底个性化医疗，同时使用最先进的技术维持高质量的医疗服务。 随之而来的是，医学界由于对基于协议的不屈服的算法患者护理的快速临床神圣判断的Swift失败而失去理智。 老式的人口健康模式是应归咎于这种做法的原因之一。 但是，如果通过透明和负责任的方法获得人工智能，则可以为个性化医疗系统奠定基础。 深度学习技术可以从出生开始就了解患者的一切，并以分散的方式保存(使用区块链技术 )，而无需将个人信息暴露给其他用途。 由患者，医生或任何其他用户(作为其数据的唯一所有者)收集和保存的数据将能够利用无人监督的DL技术来帮助他们利用他们想要和需要的个性化护理。 集中式大数据处理只会使其他行业受益，并进一步污染已经存在缺陷的人口健康模型。
Artificial Intelligence will enable physicians to tailor treatments to the patient’s needs.
The population health principle is not receptive to a patient’s individual wants. Deep learning will learn every Individual need according to his or her expectations and needs, thus by way of AI, will advise the physician and patient alike, the best.
人口健康原则不接受患者的个人需求 。 深度学习将根据他或她的期望和需求来学习每个人的需求，因此通过AI的方式，将为医生和患者提供最好的建议。
人工智能的弊端 (The Bad of Artificial Intelligence)
Modern society has been liberal in handling public digital information. But citizens are and will still pick up the consequences of such naivety of their attitude, yet, only the hard way. For instance, they will eventually figure out how valuable is what they are putting out there on harm’s way and how it is abused or used against them. Most of all, people will, in the end, realize- despite public reassurance by the giant social media and tech moguls, their data is not only a covert weapon against them but also is the digital cash they could put back in their pocket. Instead, personal data are indirectly being weaponized and laundered in the global scenery. Nevertheless, lets only hope it will not be too late; we all recognize those as mentioned earlier.
现代社会在处理公共数字信息方面一直很自由。 但是，公民现在并且仍然会承受如此幼稚态度的后果，但这只是艰难的方式。 例如，他们最终将弄清楚他们在危害方面所付出的努力是多么有价值，以及危害是如何被滥用或利用的。 最重要的是，人们最终将意识到-尽管巨大的社交媒体和科技大亨向公众保证，他们的数据不仅是对他们的秘密武器，而且还是他们可以放回口袋的数字现金。 取而代之的是，在全球范围内间接地对个人数据进行了武器和洗钱。 不过，只希望不会太晚。 我们都承认前面提到的那些。
They say, on Facebook, users are becoming more and more discreet about who they share what kinds of data with, but with the use of Un-supervised Deep learning methods, even being discrete will be superseded unless people stop using the Facebook altogether.
他们说，在Facebook上，用户对于与谁共享什么样的数据越来越谨慎，但是使用无监督深度学习方法，除非人们完全停止使用Facebook，否则即使是离散的方法也将被取代。
Likewise, True, patients tend to trust their physicians more than they might believe in a big company like Facebook, which may help alleviate discomfort with contributing data to large-scale research initiatives but what good it will do if the data is centrally stored and the giant HITECH company is the sole holder of the “Big Data”?!
同样，的确，患者对医生的信任程度超过了他们对像Facebook这样的大公司的信任程度，这可能有助于减轻为大型研究计划贡献数据时的不适感，但是如果将数据集中存储并存储在数据库中，它将有什么用处？巨型HITECH公司是“大数据”的唯一持有人吗？
自动化导致的失业 (Automation-Spurred job Loss)
Although the subject of Artificial Intelligence replacing human jobs is a matter of great controversy, yet it is the least of all concerns. AI, indeed, will replace particular types of jobs in many industries, and not exclusively those jobs requiring predictable and repetitive tasks. Nevertheless, no doubt, disruption has already begun.
尽管人工智能替代人类工作的主题存在很大争议，但它是所有关注中最少的问题。 的确，人工智能将取代许多行业中特定类型的工作，而不仅仅是需要可预测和重复性任务的工作。 然而，毫无疑问，破坏已经开始。
侵犯隐私 (Privacy Violations)
Ill-disposed use of AI could threaten digital security by various modalities is an imminent threat. Training machines to hack or socially engineered victims is a matter of great concern. Also, non-state actors weaponizing consumer drones, or privacy-eliminating surveillance, profiling, repression, automated, and targeted disinformation campaigns are a few of many fits of abuse we can face by refusing to see the trend.
对AI的不良使用可能会通过各种方式威胁数字安全 ，这是迫在眉睫的威胁。 培训黑客或受社会工程影响的受害者的机器非常令人关注。 同样，非国家行为者为消费者无人机提供武器，或消除隐私的监视，配置文件，压制，自动化和针对性的虚假信息宣传活动，都是我们拒绝看到这一趋势所能遇到的许多弊端。
利用深度学习的Deepfake (Deepfakes Utilizing Deep Learning)
Similarly, audio and video created by manipulating voices and likenesses. Deepfakes is already making waves. Using machine learning and deep learning will potentially involve natural language processing; an audio clip of any particular politician could be tainted to make it seem as if that person spurted racist views when in reality, they uttered nothing of the sort.
类似地，通过操纵声音和肖像来创建音频和视频。 Deepfakes已经在掀起波澜。 使用机器学习和深度学习可能会涉及自然语言处理； 任何特定政治人物的音频剪辑都可能受到污染，以至于该人似乎在现实中大肆宣扬种族主义观点，而他们却一言不发。
不良数据导致的社会经济不平等和算法偏差 (Socioeconomic inequality and Algorithmic bias caused by bad Data)
The widening socioeconomic disparity can be well thought using AI-driven job loss is a major concern. Along with education, work has long been also a driver of social mobility. However, when it’s a certain kind of work, the predictable, repetitive nature that’s prone to AI takeover research has given away that those who find themselves out in the cold are much less apt to get or seek retraining compared to those in higher-level positions which have more money.
可以认为，使用AI驱动的失业会加剧社会经济差距的扩大，这是一个主要问题。 随着教育的发展，长期以来，工作也是社会流动性的驱动力。 但是，当这是某种工作时，易于进行AI接管研究的可预测，重复的性质已经表明，与处于较高职位的人相比，处于冷漠中的人更不容易接受或寻求再培训。里面有更多的钱。
Various forms of AI bias are detrimental, as are the data and algorithmic bias. The latter can “amplify” the former.
各种形式的AI偏差都是有害的，数据和算法偏差也是如此。 后者可以“放大”前者。
We always need to remember; Artificial Intelligence is the product of human beings, and humans are innately influenced. AI researchers merely come from certain racial demographics, who grew up in high socioeconomic areas. Scientists are primarily people without disabilities from a fairly homogeneous population. Therefore, it’s difficult for those individuals to efficiently connect with the diversity of the society and their assorted concerns.
我们总是需要记住； 人工智能是人类的产物，人类具有天生的影响力。 人工智能研究人员仅来自某些种族人口统计学家，他们在社会经济高度发达的地区长大。 科学家主要是来自相当同质化人群的无障碍人士。 因此，这些人很难有效地与社会的多样性和他们所关注的各种问题联系起来。
The root of all biases in the process from Data Mining to deep learning and ultimately, Artificial intelligence is socially and economically motivated. Because technology is the derivative of what humans design, hence making scientists and executives some of the most treacherous people in the world by way of the illusion of objectivity and greed.
从数据挖掘到深度学习的过程中所有偏见的根源，最终，人工智能具有社会和经济动机。 因为技术是人类设计的衍生工具，所以通过客观性和贪婪的幻想，使科学家和高管成为了世界上最奸诈的人。
武器自动化 (Weapons Automatization)
Artificial Intelligence can be extra dangerous than bombs. The important is whether it is a good idea to start a global Artificial Intelligence arms race or to prevent its future proliferation. If any major military power acquires AI weapon development, a global arms race would be virtually foreseeable, and the endpoint of this technological trajectory is obvious, as autonomous weapons will become the guns of tomorrow.
人工智能比炸弹还要危险。 重要的是开始全球人工智能军备竞赛还是防止其未来扩散是一个好主意。 如果任何主要军事力量获得了AI武器的发展，那么全球军备竞赛实际上是可以预见的，随着自主武器将成为明日之枪，这种技术发展轨迹的终点是显而易见的。
Unlike nuclear weapons, AI requires no costly or hard-to-obtain raw materials. They will become ubiquitous and inexpensive for all significant military supremacies to mass-produce. It is going to only be a matter of time before the smart robotic weapons surface on the black market and in the hands of radicals, dictators wishing to better control their masses, tyrants wishing to commit ethnic cleansing, etc.
与核武器不同，人工智能不需要昂贵或难以获得的原材料。 对于所有重要的军事霸权大规模生产而言，它们将无处不在且廉价。 智能机器人武器出现在黑市和激进分子手中，独裁者们希望更好地控制群众，独裁者们希望进行种族清洗，这只是时间问题。
Self-governing armaments are flawless for jobs like subverting a country, committing murders, mollifying populations. A military AI arms war would not be propitious for humanity. There are many ways in which Artificial Intelligence can make battlegrounds safer for humans, especially civilians, without designing new tools to kill people. But then again, The US Military’s proposed budget for 2020 is $718 billion. Of the mentioned amount, nearly $1 billion would support AI and machine learning for things like logistics, intelligence analysis, and weaponry. AI can farther enhance the selective assassination of a certain ethnic group.
自治武器对于颠覆国家，犯下谋杀罪，减轻人口负担等工作而言是完美无缺的。 军事AI武器战争不会有利于人类。 人工智能可以通过多种方式为人类(尤其是平民)提供更安全的战场，而无需设计杀死人类的新工具。 但话又说回来，美国军方2020年的拟议预算为7180亿美元。 在上述金额中，将近10亿美元将用于人工智能和机器学习，用于物流，情报分析和武器装备。 人工智能可以进一步增强某些种族的选择性暗杀。
人工智能和企业大数据是《变形金刚》隐喻唯一的食物 (Artificial Intelligence and Big Data for Corporations is the food for the sole of Transformers Metaphor)
Historically corporations have enjoyed the munificence of personhood, collective influence of its stakeholder’s money, and technology. Today, using AI, corporate cartels are exercising the ability to read the human mind, access their personal information without breaking a single law. Nevertheless, the forfeits of people to the entire collective action of the industries are real.
从历史上看，公司一直享受着人性的丰富，利益相关者的金钱的集体影响以及技术。 如今，企业卡特尔正在使用AI来阅读人的思想，访问其个人信息而又不违反一项法律。 然而，人们对产业的整个集体行动的丧失是真实的。
人工智能行业正在取代医师 (Artificial Intelligence Industry is Replacing Physician)
It is the prevailing conception more so by the HITECH industry that machines will eventually replace physicians. Although this may be true, it is farther from wise. The indiscriminate utility of ML and AI is not only overwhelming to physicians’ practices but also influences the quality of care a patient receives from their provider. Building a technology that will utilize a prewritten algorithm through business intelligence or machine learning that is primarily designed to collect data from various sources is a growing and scary trend. Not just from a business perspective that it would be valuable just like a gold rush of our century but also from the quality and utilization perspective that is directly involved in the care of the patient.
HITECH行业更普遍地认为机器将最终取代医师 。 尽管这可能是正确的，但这远非明智之举。 ML和AI的不分青红皂白的用途不仅压倒了医生的实践，而且影响了患者从其提供者那里获得的护理质量。 建立一种将通过商业智能或机器学习来利用预先编写的算法的技术，该技术的主要目的是从各种来源收集数据，这是一个日益增长的可怕趋势。 不仅从业务的角度来看，它像本世纪的淘金热一样有价值，而且从直接参与患者护理的质量和使用角度来看，也是如此。
机器学习和人工智能的丑陋 (The ugly of Machine learning and Artificial Intelligence)
Data industry, Big data, and more so, Health information has turned into a money-printing engine for every single industry. Health information lone has become trillions of dollars market. Software companies persuade citizens that the data is encrypted thus not accessible, even to their own employees. But AI has provided them with the capability to utilize public data any way they please. Parallel to advancements with Deep Learning technologies, the concept of Internet Freedom and Net neutrality is becoming more and more obsolete.
数据行业，大数据等等， 健康信息已成为每个行业的赚钱引擎。 健康信息孤单已成为数万亿美元的市场。 软件公司说服市民，数据是加密的，因此，即使是对其自己的员工也无法访问。 但是AI为他们提供了以他们希望的任何方式利用公共数据的能力。 与深度学习技术的进步并行的同时， 互联网自由和网络中立的概念也越来越过时。
移情移情 (Empathic Transference)
The human being is fascinated by finding ways to teach the machine to express full empathy just like the human. The concept of Empathetic Transference is the ugly image of the human being on its way to satisfy the longtime battered ego.
通过寻找方法教机器像人一样表达出完全的同理心，使人着迷。 移情转移的概念是人类在满足长期遭受重创的自我的过程中的丑陋形象。
The imbalance between strategy and tactical mission of many industries has turned out to be the upshot of tempting Big Data gold rush, pivoting the industry away from what their vision and mission originally conveyed. The latter has been further enhanced by advances made in Deep learning schemes.
事实证明，许多行业的战略与战术任务之间的不平衡是诱使大数据淘金热的结果，使该行业偏离了他们最初传达的愿景和使命。 深度学习计划的进步进一步增强了后者。
人工授精和基因分析 (Artificial Insemination and Genetic Profiling)
Artificial Intelligence, big data, are presently used in artificial insemination, donor eggs, and genetic profiling with particular pros and cons. The latter said it- is with particular reference to the science, technology, and cultural as well as ethical applications. When applied collectively, their impact on the constancy of social norms is exponentially deleterious. We’re simply entering a space where the upkeep of anonymity, respecting individual privacy as well as preventing major social, psychological, ethical, and legal dispute will be a strenuous undertaking. Unless fundamental solutions are realized with regards to AI and DL algorithms, the era of paternal anonymity will be soon coming to an end. Between the Sperm banks being forced to break rules of a confidentiality agreement with their donors, the growing Genetic Testing Market, along with lucrative corporate financial gain; the upkeep of donor confidentiality and offspring identity is fated to become an unrelenting task.
人工智能， 大数据，目前用于人工授精，供体卵和具有特定优缺点的基因分析中 。 后者说-特别是指科学，技术，文化以及伦理应用。 当集体应用时，它们对社会规范持续性的影响成倍地有害。 我们只是进入一个空间，在这里，保持匿名，尊重个人隐私以及防止重大的社会，心理，道德和法律纠纷将是一项艰巨的任务。 除非实现有关AI和DL算法的基本解决方案，否则父权匿名的时代即将结束。 在精子银行被迫违反与捐助者的保密协议规则之间，不断增长的基因检测市场以及可观的公司财务收益； 维护捐助者的机密性和后代的身份注定将成为一项艰巨的任务。
为什么拥有算法很重要 (Why it is important to own the Algorithms)
Compassion, sentiment, empathies are all significant parts of the healing process and medical treatments. But although you can teach computers to act empathic, it will always fall short of real human emotion, that is exactly why Artificial Intelligence will never replace the physician’s role. Nevertheless, it doesn’t override the fact that physicians should not adapt themselves to the perpetual changes happening around them.
同情，情感和同理心都是康复过程和药物治疗的重要组成部分 。 但是，尽管您可以教计算机表现出同理心，但它总是会缺乏真实的人类情感，这就是为什么人工智能永远不会取代医生的角色的原因。 然而，这并不能取代医生不应适应周围发生的永久性变化这一事实。
Artificial intelligence is here and is most likely to stay. Physicians can choose to pull the blind eye on technological advancement, particularly over the DL algorithms, or they take ownership of their domain. If they select the former attitude, physicians will lose their job and sacred obligation to patients to industries and people who have little or no knowledge of patient care.
人工智能就在这里，很可能会留下来。 医师可以选择对技术进步视而不见，尤其是在DL算法方面，或者他们对自己的领域拥有所有权。 如果他们选择以前的态度，医生将失去工作，并向患者提供对行业和对患者护理知之甚少或根本不了解的人们的神圣义务。
The physicians must reform the way they practice medicine. They must harmonize and direct the way they care for a patient on the right path using the most up to date tools that are validated and tested by physicians for the healthcare community.
医师必须改革他们的医学实践方式 。 他们必须使用经过医疗保健界医师验证和测试的最新工具，在正确的道路上协调和指导他们护理病人的方式。
AI算法必须透明，并且架构师负责 (AI Algorithms must be Transparent, and Architects Accountable)
It is also crucial to maintain transparency of the algorithms if physicians and the medical community ought to ensure quality medical practice. Since trusting technology is nothing like trusting its designers, that sure stands true for Artificial Intelligence and machine learning.
如果医师和医学界应确保高质量的医疗实践，保持算法的透明度也至关重要 。 由于信任技术与信任设计者完全不同，因此对于人工智能和机器学习而言，这确实是正确的。
Accountability is a must; however, to assert culpability proceeding Artificial intelligence, first proper transparency initiatives must be implemented. Most importantly, physicians must demand such transparency and mandate the accountability if they are not to be held liable themselves. The latter is the epitome of change expected of the physician community.
问责制是必须的； 但是，要断定人工智能的罪魁祸首，必须首先实施适当的透明度计划。 最重要的是，如果医生不自己承担责任，则必须要求这种透明性并要求承担责任。 后者是医师界所期望的变化的缩影。
The legal community, particularly attornies, are facing similar challenges as the physicians; nonetheless, they seem to be effectively retaining the ownership of their artificial intelligence algorithms. All attornies have collectively established that the conflict of interest threatening the legal system by way of financial benefit over admitting “non-lawyers” to own or invest in law firms. Technically speaking, the opponents of the latter rule are particularly concerned about the method of validation of their technology. Improper validation and oversight would give rise to more effortless undertaking certain legal activities by non-attorneys.
法律界，特别是律师，面临着与医生类似的挑战； 尽管如此，它们似乎仍在有效保留其人工智能算法的所有权。 所有律师共同确定，利益冲突通过经济利益威胁法律体系，而不是允许“非律师”拥有或投资律师事务所。 从技术上讲，后一种规则的反对者特别关注其技术的验证方法。 验证和监督不当将导致非律师更轻松地进行某些法律活动。
医师必须了解的人工智能知识 (What Physicians must know about Artificial Intelligence)
The Healthcare technology rush is the major factor behind the disconnect between Physicians and their Domain and vice versa. Companies other than the health industry have alternate motives, hence mining for valuable patient and physician. The utility of Deep learning to snatch patient information has already begun. As mentioned earlier, Big data mining is vital to make available the vast pool of information required for robotic medicine and artificial intelligence. Besides, all the above is required to replace the human factor in the prospect.
医疗保健技术的迅猛发展是医师与其领域之间相互脱节，反之亦然的主要原因 。 卫生行业以外的公司还有其他动机，因此可以挖掘有价值的患者和医生。 深度学习用于抢夺患者信息的工具已经开始。 如前所述，大数据挖掘对于提供机器人医学和人工智能所需的大量信息至关重要。 此外，上述所有内容都需要替换前景中的人为因素。
Imperatively, Algorithms should deliver as they intended for tactical medical care, devoid of any strategic undertaking to pivot corporate interest to financial gain.
势在必行，算法应按预期的目的提供战术医疗服务，而没有任何将公司利益转向财务利益的战略承诺。
Physicians are the only ones who can warrant the adaptability of Deep learning algorithms to individual circumstances while designing them to act submissive to physicians versus acting as an independent provider.
医师是唯一可以保证深度学习算法适应具体情况的人，同时将其设计为对医生服从，而不是作为独立提供者。
The Medico-legal Perils of Artificial Intelligence and Deep Learning can be deleterious to physicians, if not recognized. So a valuable AI must identify the particular reference point for the standard of care on a precise scenario, time, place, and individual.
如果未被认可，那么人工智能和深度学习的法律法律风险可能对医生有害。 因此，有价值的AI必须在精确的场景，时间，地点和个人上确定护理标准的特定参考点。
The physician, with the aid of patients, ought to redefine every case and have the legal, ethical, and methodical power to mutually override verdicts, by making a personalized approach. If failed to address patient problems within the medical standard of care spectrum, the treating physician will potentially face legal responsibility if something goes wrong.
医师应在患者的帮助下重新定义每个案例，并具有通过采取个性化方法相互推翻判决的法律，道德和方法上的权力。 如果未能在医疗标准范围内解决患者问题，则如果出现问题，主治医师将可能面临法律责任。
态度的改变拯救了医师的独立性 (Change in Attitude saves Physicians Independence)
Unfortunately, the physician’s profession relies on self-created habits. Doctors’ habitual practice has formed cultures and staff hiring practices that align with those personal habits. But routines need to change, something that will further disconnect physicians from the contemporary world, if not turned around.
不幸的是， 医师的职业依赖于自己养成的习惯 。 医生的习惯做法已经形成了与这些个人习惯相一致的文化和员工聘用做法。 但是，例行程序需要改变，这将使医生与当代世界失去联系，如果不扭转局面的话。
Along the spectrum of attitude reform, physicians must embrace Artificial intelligence, deep learning technology, just like they did embrace stethoscope and X-ray during the past centuries. Doctors must understand its utility and perils. Only then can they adapt the best practice, maintain independence, ensure patient safety, and promote modern personalized healthcare.
在态度改革的整个过程中，医生必须拥抱人工智能和深度学习技术，就像他们在过去几个世纪中拥抱听诊器和X射线一样。 医生必须了解其效用和风险。 只有这样，他们才能适应最佳实践，保持独立性，确保患者安全并促进现代个性化医疗保健。
This article was originally published by Data Driven Investor